



"""simple docstring"""






import argparse
import logging
import os
import sys

import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers

import transformers
from transformers import BartForConditionalGeneration, BartTokenizer


logging.basicConfig(
    format='''%(asctime)s | %(levelname)s | %(name)s |  [%(filename)s:%(lineno)d] %(message)s''',
    datefmt='''%Y-%m-%d %H:%M:%S''',
    level=os.environ.get('''LOGLEVEL''', '''INFO''').upper(),
    stream=sys.stdout,
)

lowerCAmelCase__              =							logging.getLogger(__name__)

lowerCAmelCase__              =							{'''facebook/bart-base''': BartForConditionalGeneration}
lowerCAmelCase__              =							{'''facebook/bart-base''': BartTokenizer}






def 			snake_case_    ( ):


       '''simple docstring'''





       _lowerCamelCase		:							str            =					argparse.ArgumentParser(description='''Export Bart model + Beam Search to ONNX graph.'''		)
       parser.add_argument(
           '''--validation_file''',							type=A_,							default=A_,							help='''A csv or a json file containing the validation data.'''		)
       parser.add_argument(
           '''--max_length''',							type=A_,							default=5,							help='''The maximum total input sequence length after tokenization.''',							)
       parser.add_argument(
           '''--num_beams''',							type=A_,							default=A_,							help=(
               '''Number of beams to use for evaluation. This argument will be '''
               '''passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.'''
           ),							)
       parser.add_argument(
           '''--model_name_or_path''',							type=A_,							help='''Path to pretrained model or model identifier from huggingface.co/models.''',							required=A_,							)
       parser.add_argument(
           '''--config_name''',							type=A_,							default=A_,							help='''Pretrained config name or path if not the same as model_name''',							)
       parser.add_argument(
           '''--device''',							type=A_,							default='''cpu''',							help='''Device where the model will be run''',							)
       parser.add_argument('''--output_file_path''',							type=A_,							default=A_,							help='''Where to store the final ONNX file.'''		)

       _lowerCamelCase		:							Optional[int]            =					parser.parse_args()

       return args






def 			snake_case_    ( A_   :						Optional[int],							A_   :						Tuple="cpu"		):


       '''simple docstring'''





       _lowerCamelCase		:							Dict            =					model_dict[model_name].from_pretrained(A_		).to(A_		)
       _lowerCamelCase		:							Optional[Any]            =					tokenizer_dict[model_name].from_pretrained(A_		)

       if model_name in ["facebook/bart-base"]:
              _lowerCamelCase		:							int            =					0
              _lowerCamelCase		:							Any            =					None
              _lowerCamelCase		:							str            =					0

       return huggingface_model, tokenizer






def 			snake_case_    ( A_   :						Optional[int],							A_   :						Union[str, Any],							A_   :						str,							A_   :						List[str],							A_   :						Any		):


       '''simple docstring'''





       model.eval()

       _lowerCamelCase		:							Union[str, Any]            =					None
       _lowerCamelCase		:							Optional[Any]            =					torch.jit.script(BARTBeamSearchGenerator(A_		)		)

       with torch.no_grad():
              _lowerCamelCase		:							Optional[Any]            =					'''My friends are cool but they eat too many carbs.'''
              _lowerCamelCase		:							Optional[Any]            =					tokenizer([ARTICLE_TO_SUMMARIZE],							max_length=10_24,							return_tensors='''pt'''		).to(model.device		)

              _lowerCamelCase		:							Tuple            =					model.generate(
                  inputs['''input_ids'''],							attention_mask=inputs['''attention_mask'''],							num_beams=A_,							max_length=A_,							early_stopping=A_,							decoder_start_token_id=model.config.decoder_start_token_id,							)

              torch.onnx.export(
                  A_,							(
                      inputs['''input_ids'''],
                      inputs['''attention_mask'''],
                      num_beams,
                      max_length,
                      model.config.decoder_start_token_id,
                  ),							A_,							opset_version=14,							input_names=['''input_ids''', '''attention_mask''', '''num_beams''', '''max_length''', '''decoder_start_token_id'''],							output_names=['''output_ids'''],							dynamic_axes={
                      '''input_ids''': {0: '''batch''', 1: '''seq'''},
                      '''output_ids''': {0: '''batch''', 1: '''seq_out'''},
                  },							example_outputs=A_,							)

              logger.info('''Model exported to {}'''.format(A_		)		)

              _lowerCamelCase		:							int            =					remove_dup_initializers(os.path.abspath(A_		)		)

              logger.info('''Deduplicated and optimized model written to {}'''.format(A_		)		)

              _lowerCamelCase		:							Tuple            =					onnxruntime.InferenceSession(A_		)
              _lowerCamelCase		:							int            =					ort_sess.run(
                  A_,							{
                      '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
                      '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
                      '''num_beams''': np.array(A_		),
                      '''max_length''': np.array(A_		),
                      '''decoder_start_token_id''': np.array(model.config.decoder_start_token_id		),
                  },							)

              np.testing.assert_allclose(summary_ids.cpu().numpy(),							ort_out[0],							rtol=1E-3,							atol=1E-3		)

              logger.info('''Model outputs from torch and ONNX Runtime are similar.'''		)
              logger.info('''Success.'''		)






def 			snake_case_    ( ):


       '''simple docstring'''





       _lowerCamelCase		:							str            =					parse_args()
       _lowerCamelCase		:							List[Any]            =					5
       _lowerCamelCase		:							Any            =					4

       # Make one log on every process with the configuration for debugging.
       logging.basicConfig(
           format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',							datefmt='''%m/%d/%Y %H:%M:%S''',							level=logging.INFO,							)

       logger.setLevel(logging.INFO		)
       transformers.utils.logging.set_verbosity_error()

       _lowerCamelCase		:							Optional[Any]            =					torch.device(args.device		)

       _lowerCamelCase     ,       _lowerCamelCase		:							int            =					load_model_tokenizer(args.model_name_or_path,							A_		)

       if model.config.decoder_start_token_id is None:
              raise ValueError('''Make sure that `config.decoder_start_token_id` is correctly defined'''		)

       model.to(A_		)

       if args.max_length:
              _lowerCamelCase		:							Union[str, Any]            =					args.max_length

       if args.num_beams:
              _lowerCamelCase		:							Optional[int]            =					args.num_beams

       if args.output_file_path:
              _lowerCamelCase		:							List[str]            =					args.output_file_path
       else:
              _lowerCamelCase		:							Union[str, Any]            =					'''BART.onnx'''

       logger.info('''Exporting model to ONNX'''		)
       export_and_validate_model(A_,							A_,							A_,							A_,							A_		)


if __name__ == "__main__":
   main()



"""simple docstring"""



from __future__ import annotations

from math import pi



def 	_lowerCamelCase(	a   ,    a   ,    a				):

       if (inductance, frequency, reactance).count(0				) != 1:
              raise ValueError("One and only one argument must be 0"				)
       if inductance < 0:
              raise ValueError("Inductance cannot be negative"				)
       if frequency < 0:
              raise ValueError("Frequency cannot be negative"				)
       if reactance < 0:
              raise ValueError("Inductive reactance cannot be negative"				)
       if inductance == 0:
              return {"inductance": reactance / (2 * pi * frequency)}
       elif frequency == 0:
              return {"frequency": reactance / (2 * pi * inductance)}
       elif reactance == 0:
              return {"reactance": 2 * pi * frequency * inductance}
       else:
              raise ValueError("Exactly one argument must be 0"				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
